Robust Region Feature Extraction With Salient MSER and Segment Distance-Weighted GLOH for Remote Sensing Image Registration

Remote sensing image registration is one of the crucial steps in remote sensing image processing, where ground control information is essential. Maintenance of control point databases is complex and expensive. Consequently, lightweight feature databases are emerging. Lightweight feature databases need to store stable and reproducible features. In this context, region features exhibit a distinct advantage. In feature registration methods, the reproducibility of regional features is typically stronger than with individual points. A popular feature region matching method is currently the combination of maximally stable extremal regions (MSER) and scale-invariant feature transform (SIFT). However, the direct combining of MSER and SIFT has difficulties primarily due to redundancy and overlap in regions extracted by MSER, as well as the conflict in applying texture descriptors on homogeneous regions. In this research, we first suggest a salient MSER detection approach that combines frequency-tuned salient region detection and effective nonmaximum suppression filtering to get rid of redundant information and enhance the stability and dependability of the feature region; afterward, we describe the feature region using the unique, enhanced segment distance-weighted gradient location-orientation histogram, which aims to comprehensively describe the feature regions by incorporating more information about the gradient at the edges of the regions. In the experimental phase, we validate the proposed method using multiple remote sensing images. The experimental results confirm the superiority of the proposed method and demonstrate the significant potential and advantages of feature region matching in the context of lightweight feature databases and remote sensing image registration.